The present invention relates to compression and transmission of a digital image signal using an orthogonal transformation process and quantization of the thus-obtained transform coefficients to produce compressed data.
A digital VTR which records a digital video signal on a magnetic tape with, for example, rotary heads, typically compresses the digital data using a highly efficient coding technique before recording the data. One such coding technique is a discrete cosine transformation (DCT), in which one frame of an image is converted into blocks of, for example, (8.times.8) elements. Each of the image data blocks is then transformed into a block of (8.times.8) coefficient data which are compressed using a variable length code encoding process, such as a run length code encoding process followed by a Huffman code encoding process. The thus compressed data comprise a code signal which undergoes frame segmentation, that is, the code signal is placed into data areas of a predetermined number of sync blocks, and combined with a synchronization signal and an ID signal. The frame segmented signal is transmitted to the rotary heads for recording.
To control the amount of data recorded on one track to be a predetermined value, before the coefficient data are encoded with a variable length code, the coefficient data are quantized using a quantization step size chosen to produce a certain amount of quantized data. It is preferred that the data amount control, also referred to as the buffering process, consider the frequency distribution of the coefficient data and the effect of the quantization on the image quality of the reproduced image.
Specifically, high frequency transform coefficients are usually quantized with a large quantization step size, resulting in a coarsely quantized image, whereas low frequency transform coefficients are quantized with a small quantization step size, resulting in a finely quantized image. This procedure results in more quantization distortion in the high frequency components of a reproduced image than in its low frequency components, which is acceptable since quantization distortion in the high frequency components of an image is less noticeable than quantization distortion in the low frequency components of an image.
To reduce the quantization noise in the high frequency components, a method using both a round-off process and a truncation process has been proposed in which a round-off process is used when quantizing high frequency coefficients and a truncation process is used when quantizing low frequency coefficients. In this method, since the pictorial pattern of a block is considered, the deterioration in quality of a reproduced image can be prevented to some extent. However, the round-off process adversely affects the signal to noise (S/N) ratio of the reproduced signal.
Quantization distortion is less noticeable in a fine pictorial pattern than in a pattern representing an edge. A block with a fine pictorial pattern has high frequency components. However, if the high frequency components in a block represent an edge, then distortion due to quantization of the high frequency coefficient data is noticeable.